Simone Grechi

The project V-fides: A new generation AUV for deep underwater exploration, operation and monitoring

The proposed work is in the framework of the V-Fides project, aiming at developing a new generation of agile, over-actuated, long endurance Autonomous Underwater Vehicles, for deep underwater exploration, operation and monitoring. The project is co-funded by Tuscany Region (Italy) and is developed by a team lead by WASS S.p.A. (Whitehead Sistemi Subacquei, Livorno) with the participation of several partners including two research institutions of the University of Pisa and Small-Medium Enterprises in the Pisa-Livorno area. The vehicle is a general purpose, 3000m depth rated underwater vehicle with highly maneuverability capabilities, which can operate both as AUV and ROV. The vehicle is equipped with seven thrusters, with asymmetric input-output characteristic, and with a sensors payload for autonomous navigation, composed by: a tactical grade Inertial Measurement Unit (IMU), a Doppler Velocity Logger (DVL), a depth sensor, a magnetic compass and an acoustic modem for underwater communication and localization. This contribution gives an overview of the developed general architecture of the Navigation and Control module of the vehicle, from the algorithmic and system implementation stand-points.

OptoCOMM: Development and experimentation of a new optical wireless underwater modem

This paper presents our recent advancements in the development of the underwater optical wireless modems, designed within the OptoCOMM project. The target performance of the optical modem is to achieve the data rate of 10 Mbit/s at a distance of at least 10 m in presence of sunlight. The transmitter is based on blue Light Emitting Diode (LED) technology. The modem will be developed in three versions to be integrated in Littoral Ocean Observatory Network (LOON) test-bed, and add a novel technology to the infrastructure of the FP7-SUNRISE project. We tested the first version of the modems in a typical shallow harbour water (1 m depth), in the same location of the LOON to validate the adopted technology. The seawater of the test-bed is characterized by a medium-high turbidity (about 1 FTU), which heavily affects the optical communication in terms of attenuation. All tests were run in sunny summer days with very high sunlight illumination level (~ 100 klux). Despite the challenging conditions, we achieved successful transmission at 10 Mbit/s over 7.5 m distance.

OptoCOMM and SUNSET to enable large data offloading in Underwater Wireless Sensor Networks

In this paper we present the initial implementation of an integrated optical and acoustic system that can enable large data transfer between mobile and static nodes in Underwater Wireless Sensor Networks (UWSNs). The proposed system is based on the OptoCOMM optical modem and on the SUNSET Software Defined Communication Stack (S-SDCS) framework. The OptoCOMM modem allows to overcome the limits of maximum data rate and bandwidth imposed by the use of acoustic communication by providing a data rate of 10Mbps. SUNSET SDCS instead has been used to provide networking and fragmentation capabilities to efficiently offload large data in UWSNs. The performance of the proposed approach has been evaluated through in lab experiments where large files with arbitrary sizes have been optically transferred. The results achieved show that our system is able to transfer up to 1.5 GBytes of data in short time.

A NLPCA hybrid approach for AUV thrusters fault detection and isolation

The objective of this paper is to address the problem of Fault Detection and Isolation (FDI) on thrusters of an over-actuated Autonomous Underwater Vehicle (AUV) under on/off abrupt faults. The goal is pursued through Non-Linear Principal Component Analysis (NLPCA), which is the non-linear extension of the popular Principal Component Analysis (PCA). While the Fault Detection (FD) system directly exploits the model-free nature of NLPCA (data-driven approach), the Fault Isolation (FI) is achieved by properly train off-line Artificial Neural Network (ANN). The consistency and robustness of the proposed method is verified in realistic simulation.